We use the Argoverse 1.1 dataset [1]}, which is a widely used dataset for motion forecasting to evaluate our method. The Argoverse 1.1 dataset consists of real-world driving scenarios with 205,942 training samples, 39,472 validation samples, and 78,143 test samples. For training and validation samples, the dataset provides a 2 second history and a 3 second future trajectories sampled at 10 Hz. For the test samples, only the 2 second history is provided. The dataset is collected from two cities, Miami and Pittsburgh. In addition to the agent trajectories, the map information is also provided in the form of a lane graph. It represents lanes as lane centerlines, which is a sequence of points, with lane attributes including turn direction, presence of traffic control, and presence of intersections. To construct the reversed lane features required by cycle consistency, we reverse the turn directions and reverse the order of points in each centerline. Argoverse is a single-agent dataset that only requires the models to predict the trajectories for one single target agent in each frame. Therefore, using our terminology described in Section REF , for the Argoverse dataset, \(H=20\) , \(F=30\)  and \(P=1\) .
